Threshold - Urban Development & Service Gap Analysis Platform Threshold is a comprehensive geospatial analysis platform that combines satellite remote sensing, machine learning, and real-time data processing to identify optimal locations for urban development and analyze service accessibility gaps.
Binary classification experiments to interpret Google AlphaEarth Foundation embeddings across ESA WorldCover land cover classes. Part of the study "What on Earth is AlphaEarth?" — 130,000+ experiments using Random Forest, XGBoost, LightGBM and progressive ablation across 64 embedding dimensions.
Reproducible openEO workflow for monthly NDVI time-series analysis of land-cover classes in Borgo San Lorenzo, Italy.
Linear-probe land-cover classifier on precomputed Tessera (Sentinel-1+2 foundation model) embeddings — one tile, one day, CPU-only.
Ongoing master's thesis titled "Enhancing LULC Classification Using Satellite Image Embeddings and Machine Learning in A Data-scarce Region: A Case Study of Timor-Leste". This study aims to provide a machine learning framework that can support LULC monitoring in data-limited region such as Timor-Leste.
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